Direct Sensing of Remote Nuclei: Expanding the Reach of Cross-Effect Dynamic Nuclear Polarization
Bibliographic record
Abstract
Dynamic Nuclear Polarization (DNP) has revolutionized the field of solid-state NMR spectroscopy by significantly enhancing the sensitivity of nuclear magnetic resonance experiments. Conventionally, cross effect DNP relies on biradicals to transfer polarization from coupled electron spins to nearby nuclear spins and subsequent relay to target nuclei via spin diffusion mechanism. However, the direct transfer of polarization to distant nuclei remains a significant challenge, limiting its applicability in various contexts. In this work, we propose a novel biradical design concept that involves a very strong electron-electron coupling, with a magnitude of hundreds of MHz, which enables efficient direct polarization transfer from electron spins to nuclear spins over much longer distances, exceeding 2.0 nm. We discuss the potential of this tailored biradicals in scenarios where conventional spin diffusion mechanisms are inefficient or when direct nuclear spin sensing through electron spin interactions is desired. Our study presents a promising avenue for expanding the scope of cross effect DNP in solid-state NMR spectroscopy and opens new opportunities for investigating a wide range of biological and material systems. Our research also provides insight into the DNP buildup time of commercially available biradicals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".